Bibliographic record
Abstract
For the past thirty years pig genetics has enjoyed a clear message from its end–users: reduce backfat and production costs. During that period, genetics, nutrition and health have together delivered improvements of some 60% in lean growth rate and feed efficiency. To compound the recent misfortunes of the UK industry, meat is now slipping further behind everything else on the supermarket shelf in quality, uniformity, and above all predictability. The notion of quality stretches far beyond the product into responsibility for animal welfare, human nutrition and food safety. The industry's present dilemma arises from five factors: 1. uncertain market conditions with cyclical profitability 2. poor communication of what constitutes good quality 3. payment systems that no longer reflect what the market requires 4. independent management of the different steps in the pork value chain 5. possible effects of animal health on quality and uniformity. Meanwhile the understanding of gene function and the ability to detect potentially useful genetic variation is gathering momentum. This paper examines the role that genetics can play in adding value, reducing risk and differentiating the product, from the perspective of a large vertically coordinated pork producer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.122 | 0.079 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".